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The strategy trades Kalshi’s Bitcoin 15-minute market, evaluating every 10 seconds. It enters 500-contract yes or no positions when BTC price trends relative to VWAP and moving averages align with low spreads and certain market prices, plus time-based entries near expiry. Exits are tiered profit/loss levels scaled to position size, with an overall cap of 2,000 contracts.
Over the May 14 to Jun 11 window, this custom strategy on Kalshi turned in +$513,111 of simulated profit (+25655.6% on its configured risk capital), at a 2.12 Sharpe. It placed 5549 simulated trades and won 68.1% of them — a high hit rate — against shallow worst peak-to-trough drawdown of -$2,442.
Under the hood it simulated 1653 Bitcoin (BTC) markets, closing 1170 winning and 548 losing positions after $34,480 in modeled fees, an average of 185.0 trades a day. That trade-by-trade detail, the equity curve above, and the full rule set below are what separate this page from a one-line leaderboard entry.
Net PnL is the headline here; the Sharpe is unannualized over this short window, so read it as a within-sample texture of the equity curve rather than an industry-standard risk score. Because every figure comes from a single 30-day historical replay, it is best treated as a hypothesis to pressure-test rather than a forecast — the same rules can behave very differently once live fills, API latency, and shifting volatility enter the picture.
This backtest runs against Bitcoin (BTC) markets on Kalshi's 15-minute series across 30 days (May 14 to Jun 11). These are short-horizon contracts that open and settle on a fixed 15-minute cadence, so the strategy is measured across many independent events rather than one long trend. Rules are evaluated once per 15-minute candle, and a signal can fill no earlier than the next tradable candle at top-of-book prices, net of Kalshi-style taker fees.
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